Embedded Infrastructure Engineer, Chanakya

Ovii's Interpretation of the Role

The Embedded Infrastructure Engineer builds and operates terabyte‑scale data storage and ingestion platforms that power AI deployments at client sites. You will design storage architectures, create reliable pipelines, and ensure performance in air‑gapped or on‑prem environments.

Role Snapshot

• Design terabyte‑scale storage architectures
• Build robust ingestion pipelines
• Optimize indexing and query performance
• Deploy databases in air‑gapped/on‑prem settings
• Implement observability and capacity planning
• Collaborate with data scientists and product teams

Must-Have Requirements

• Python
• Go
• PostgreSQL / MongoDB / Elasticsearch / ClickHouse (any two)
• Apache Kafka / Spark / Airflow / Flink / dbt (any one)
• Terraform
• data platform engineering
• large‑scale storage management
• high‑throughput ingestion pipelines

Nice-to-Have Signals

• Vector databases (Milvus, Weaviate, Qdrant, pgvector)
• Docker & Kubernetes
• Object storage (S3/MinIO)
• Columnar formats (Parquet, ORC)
• vector database implementations
• air‑gapped/on‑prem deployments

Work Setup

• Location: Delhi, India
• Work mode: ONSITE
• Employment type: Full-Time

Not Specified in JD

• Visa sponsorship
• Salary range
• Remote eligibility
• Education requirement
• Certifications
• Relocation
• Notice period
• Travel
• Security clearance
• Coding test
• Portfolio
• GitHub
• Writing sample
• Cover letter

What You'll Likely Work On

• Design and operate relational, document, vector and object storage systems handling multi‑terabyte datasets
• Create and maintain batch and streaming ingestion pipelines with monitoring, error handling and back‑pressure control
• Implement indexing, partitioning and query‑optimisation strategies for low‑latency AI data access
• Translate data‑scientist ontologies into performant physical data models
• Deploy and manage databases, vector stores and search infrastructure in air‑gapped or on‑prem environments
• Build observability for pipeline health, storage utilisation and query performance
• Own capacity planning and scaling decisions for assigned client deployments
• Partner with product and engineering teams to feed platform learnings back into core tooling

Good Fit If You Have

• Experience with vector or embedding stores is a plus
• Familiarity with containerisation and orchestration (Docker, Kubernetes)
• Background in air‑gapped or on‑premise infrastructure deployments